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All Journal JUITA : Jurnal Informatika KOMPUTIKA - Jurnal Sistem Komputer
Putra, Muhammad Ricky Perdana
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : JUITA : Jurnal Informatika

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A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) Putra, Muhammad Ricky Perdana; Utami, Ema
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24061

Abstract

The problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and Naïve Bayes algorithms, then meta model with XGBoost. The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. The BEL model evaluation results obtained an accuracy value of 90.16%, precision of 85.64%, recall of 97.31%, F1-Score of 91.10%, and AUC of 92.83%.
Co-Authors Ema Utami Juwariyah, Siti Muhammad Ridwan Robert Marco, Robert